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Computational Efficiency

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Methods and optimizations to reduce the computational resources (e.g., time, memory, or power) needed for training and inference in models. This involves techniques that streamline processing, optimize algorithms, or leverage hardware to enhance performance without compromising accuracy.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/nkusyl/evit

공식 코드

EViT: An Eagle Vision Transformer with Bi-Fovea Self-Attention

https://github.com/nkusyl/fvit

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FViT: A Focal Vision Transformer with Gabor Filter

https://github.com/nliulab/sim-shapley

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SIM-Shapley: A Stable and Computationally Efficient Approach to Shapley Value Approximation

https://github.com/nslavov/RCweb

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Inference of Sparse Networks with Unobserved Variables. Application to Gene Regulatory Networks

https://github.com/numenta/htmpapers

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How Can We Be So Dense? The Benefits of Using Highly Sparse Representations

https://github.com/nzilberstein/Repulsive-score-distillation-RSD-

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Repulsive Latent Score Distillation for Solving Inverse Problems

https://github.com/ohsngjun/3D-4DGS

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Hybrid 3D-4D Gaussian Splatting for Fast Dynamic Scene Representation

https://github.com/oldsweet/hsrmamba

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HSRMamba: Efficient Wavelet Stripe State Space Model for Hyperspectral Image Super-Resolution

https://github.com/olivesgatech/gradcon-anomaly

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Backpropagated Gradient Representations for Anomaly Detection

https://github.com/omarsherif200/hdkd

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HDKD: Hybrid Data-Efficient Knowledge Distillation Network for Medical Image Classification

https://github.com/omendibleba/considerations_for_mlps_fes

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Considerations in the use of ML interaction potentials for free energy calculations

https://github.com/omerb01/puq

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Principal Uncertainty Quantification with Spatial Correlation for Image Restoration Problems

https://github.com/open-mmlab/mmdetection3d

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An Empirical Study of Training State-of-the-Art LiDAR Segmentation Models

https://github.com/opengvlab/omniquant

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OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

https://github.com/opengvlab/piip

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Parameter-Inverted Image Pyramid Networks

https://github.com/openrlhf/openrlhf

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REINFORCE++: A Simple and Efficient Approach for Aligning Large Language Models

https://github.com/osilab-kaist/komet-benchmark-dataset

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Benchmark Dataset for Precipitation Forecasting by Post-Processing the Numerical Weather Prediction

https://github.com/oswaldhe/intar

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InTAR: Inter-Task Auto-Reconfigurable Accelerator Design for High Data Volume Variation in DNNs

https://github.com/oucailab/msfmamba

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MSFMamba: Multi-Scale Feature Fusion State Space Model for Multi-Source Remote Sensing Image Classification

https://github.com/oudeng/fosa

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Missing Data Imputation Based on Dynamically Adaptable Structural Equation Modeling with Self-Attention

https://github.com/oudeng/sesa

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Missing Data Imputation Based on Dynamically Adaptable Structural Equation Modeling with Self-Attention

https://github.com/owen718/fiveaplus-network

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Five A$^{+}$ Network: You Only Need 9K Parameters for Underwater Image Enhancement

https://github.com/ozan-oktay/Attention-Gated-Networks

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Attention U-Net: Learning Where to Look for the Pancreas

https://github.com/panagiotisanagnostou/hipart

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HiPart: Hierarchical Divisive Clustering Toolbox

https://github.com/panchgonzalez/nmor

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Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems

https://github.com/pangjh3/anllm

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Anchor-based Large Language Models

https://github.com/pansirui/TLSM_Seismic_Denoising_Demo

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Triply Laplacian Scale Mixture Modeling for Seismic Data Noise Suppression

https://github.com/panxulab/eps-multi-agent-thompson-sampling

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Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs

https://github.com/panxulab/optimal-batched-linear-bandits

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Optimal Batched Linear Bandits

https://github.com/paragduttaiisc/temporal_event_set_modeling

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Deep Representation Learning for Prediction of Temporal Event Sets in the Continuous Time Domain

https://github.com/pardistaghavi/swinmtl

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SwinMTL: A Shared Architecture for Simultaneous Depth Estimation and Semantic Segmentation from Monocular Camera Images

https://github.com/patmercier/PLL

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Linear Periodically Time-Variant Digital PLL Phase Noise Modeling Using Conversion Matrices and Uncorrelated Upsampling

https://github.com/patrick221215/abmegd

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Antibody Design and Optimization with Multi-scale Equivariant Graph Diffusion Models for Accurate Complex Antigen Binding

https://github.com/paulhausner/neural-incomplete-factorization

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Neural incomplete factorization: learning preconditioners for the conjugate gradient method

https://github.com/pbonazzi/eva

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RGB-Event Fusion with Self-Attention for Collision Prediction

https://github.com/pcl3dv/mgstream

공식 코드

MGStream: Motion-aware 3D Gaussian for Streamable Dynamic Scene Reconstruction

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.